py_crbm.py 文件源码

def conv4d(x, weights, bias, output):
    # print 'called'
    assert len(x.shape) == 4 and len(output.shape) == 4
    batch_size, input_channel = x.shape[:2]
    output_batch_size, output_channel = output.shape[:2]
    num_filters, filter_channel = weights.shape[:2]
    assert batch_size == output_batch_size, '%d vs %d' % (batch_size, output_batch_size)
    assert output_channel == num_filters
    assert filter_channel == input_channel

    # func = convolve if true_conv else correlate
    for img_idx in range(batch_size):
        for c in range(output_channel):
            output[img_idx][c] = (correlate(x[img_idx], weights[c], mode='valid')
                                  + bias[c].reshape((1, 1, 1)))
            # if img_idx == 0 and c == 0:
            #     print output[img_idx][c]
            #     print bias[c].reshape((1, 1, 1))